@inproceedings{yousofi-bhattacharyya-2024-reconsidering,
    title = "Reconsidering {SMT} Over {NMT} for Closely Related Languages: A Case Study of {P}ersian-{H}indi Pair",
    author = "Yousofi, Waisullah  and
      Bhattacharyya, Pushpak",
    editor = "Lalitha Devi, Sobha  and
      Arora, Karunesh",
    booktitle = "Proceedings of the 21st International Conference on Natural Language Processing (ICON)",
    month = dec,
    year = "2024",
    address = "AU-KBC Research Centre, Chennai, India",
    publisher = "NLP Association of India (NLPAI)",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.icon-1.17/",
    pages = "149--156",
    abstract = "This paper demonstrates that Phrase-Based Statistical Machine Translation (PBSMT) can outperform Transformer-based Neural Machine Translation (NMT) in moderate-resource scenarios, specifically for structurally similar languages, Persian-Hindi pair in our case. Despite the Transformer architecture`s typical preference for large parallel corpora, our results show that PBSMT achieves a BLEU score of 66.32, significantly exceeding the Transformer-NMT score of 53.7 ingesting the same dataset."
}